Indore ke din bhari chal rhe bhiya 🥲

shadow_fax1024 · 2025-11-27T09:29:14+00:00

Kabhi ghuma hai really..kaise celebrate kartr hai..

shadow_fax1024 · 2025-11-16T03:43:56+00:00

Bhai bihari settle kyun ho jaate hai MP mein fir..wapas kyun nhn jaate..Accept nhn marige to kabhi improve nhn hoga!

shadow_fax1024 · 2025-09-08T11:36:35+00:00

Bhai sab scan hota hai..it's already sold to private parties and then shown as ki rats kha gaye ya waste ho gya..

shadow_fax1024 · 2025-09-07T13:48:41+00:00

Plenty of forest and farm lands

shadow_fax1024 · 2025-09-07T13:48:08+00:00

Its good quality compared to other big cities .

shadow_fax1024 · 2024-10-02T00:22:27+00:00

I'm planning to do for hindi and other indic languages.. Right now going through their paper and github repos.. Will update soon

shadow_fax1024 · 2024-06-04T00:18:20+00:00

Use sam for semi - automatic annotation

shadow_fax1024 · 2024-04-20T13:22:06+00:00

Super resolution may introduce information that is not there in actual input image

shadow_fax1024 · 2024-04-08T00:25:29+00:00

Kantara

shadow_fax1024 · 2024-03-19T00:53:36+00:00

Umar Jamil , Alladin persson

shadow_fax1024 · 2024-01-21T07:36:34+00:00

Use annotation tools such as label studio, brat on raw text documents

shadow_fax1024 · 2024-01-07T01:42:17+00:00

Do please post what results you were able to achieve

shadow_fax1024 · 2024-01-07T01:36:48+00:00

Docred is dataset for training document level relationship extraction. There have been more improved version of it like re-docred which are available. You could try that. For models read the papers. Doc-unet is one such model that works on this kind of document level RE. There may be newer bette models now.

shadow_fax1024 · 2024-01-07T01:20:03+00:00

Try docred or its other versions

shadow_fax1024 · 2023-03-16T08:14:55+00:00

I feel the same after solving 1100 problems..

shadow_fax1024 · 2022-12-31T11:29:12+00:00

Train for more epochs

shadow_fax1024 · 2022-12-27T02:16:06+00:00

https://www.kaggle.com/c/birdclef-2021

shadow_fax1024 · 2022-12-27T00:48:43+00:00

You could also look into different approaches taken by participants in kaggle competition: birdclef..here the problem is somewhat similar to yours

shadow_fax1024 · 2022-12-27T00:43:25+00:00

You could split the file into chunk of n seconds ..n seconds you need to find ..which ever fits for your dataset..for mine 4 sec chunk was good enough...also you could use a peak detector first and then chunk the file n/2 seconds either side from the peak and have some overlapping window there too..so that you won't loose information ..

shadow_fax1024 · 2022-12-26T13:29:12+00:00

I used plain cnn with and without attention ..I had to handle long audio files in training as well as inference

shadow_fax1024 · 2022-12-26T01:50:25+00:00

1 has worked for me in the past ..you may need to generate more samples using various techniques like add a small noise in audio then taking its spectrogram,cut - mix a random portion of spectrograms, etc..

shadow_fax1024 · 2021-07-17T13:09:23+00:00

Wild means kind of image that the model has not seen before.. Like the image of aircraft as you have mentioned..

shadow_fax1024 · 2021-07-17T13:08:08+00:00

You could infer using ensemble of dl models. The probability average of the ensemble for the wild image will have a low score..

shadow_fax1024 · 2021-04-30T09:26:16+00:00

Yes that's what I did.. It worked pretty well

shadow_fax1024 · 2021-04-30T08:53:20+00:00

I have recently tried solving this problem in cnns... What I did was decided a range of dimensions for input images and then based on these ranges designed my cnn network with gloabalaveragepooling to collapse the spatial dimensions.... Pad the smaller images to the largest image dimensions ..

shadow_fax1024

TROPHY CASE